Abstract: Aiming at solving the problems that traditional cluster segmentation algorithms are sensitive to noise and outliners, a remote sensing image segmentation algorithm was introduced based on scanning clustering theory according to the main body expression capacity of image of RGB eigenspace and neighbor correlation of image space, which has a higher level of noise and outliners robustness. This algorithm models the RGB eigenspace distribution of different targets using ellipsoidal objects. In the model, three principle component directions approach three axis of the ellipsoid. By adjusting the ellipsoid center, principle component direction and the length of three axis, the ellipsoid will be a best fit for target bodies in the distribution of RGB eigenspace. In this case, the recognition of different target bodies in RGB eigenspace will be achieved. RGB eigenspace is capable to distinguish different type of targets effectively. However, it cannot determine subordination of the body pixels away from clusters. For fully use of the characteristics that neighbor pixels are more likely to be subordinate to the same target in the image space, neighbor pixel symbols are used to fill the holes in the result of body segmentation for getting a complete segmentation result which has a better robustness of noise. According to the experiments of synthesis remote sensing images and truth remote sensing images, not only can the proposed algorithm differentiate body targets effectively, but also improve the noise and outliners robustness greatly.